updated content

Author: Milstein

docs/openshift-ai/data-science-project/model-serving-in-the-rhoai.md

@@ -244,7 +244,7 @@ window, click **Add** to create the model server.
 ---
 
 For our example project, we will choose "Multi-model serving platform" and then
-add a new model server and let's name the **Model server** as "coolstore-modelserver".
+add a new model server and let's name the **Model server** "coolstore-modelserver".
 We'll select the **OpenVINO Model Server** in **Serving runtime**.
 
 Please leave the other fields with the default settings such as Leave **replicas**

docs/openshift-ai/data-science-project/testing-model-in-the-rhoai.md

@@ -64,7 +64,7 @@ in an augmented reality way.
 The different components of this intelligent application are:
 
 • **The Frontend**: a [React](https://react.dev/) application, typically running
-on the browser of your phone or web browser.
+in your phone's browser or a web browser on your computer.
 
 • **The Backend**: a NodeJS server, serving the application and relaying API calls,
 

docs/openshift-ai/data-science-project/using-projects-the-rhoai.md

@@ -110,7 +110,7 @@ Once you have entered the information for your workbench, click **Create**.
 For our example project, let's name it "Tensorflow Workbench". We'll select the
 **TensorFlow** image, choose a **Deployment size** of **Small**,
 **Accelerator** of **NVIDIA A100 GPU**, **Number of accelerators**
-as **1** and allocate a **Cluster storage** space of **20GB** (By Default Selected).
+as **1** and allocate a **Cluster storage** space of **20GB** (Selected By Default).
 
 After creating the workbench, you will return to your project page. It shows the
 status of the workbench as shown below:

docs/openshift-ai/other-projects/LLM-client-AnythingLLM.md

@@ -52,7 +52,7 @@ chatbot solution.
     For our example project, let's name it "AnythingLLM Workbench". We'll select
     the **AnythingLLM** image, choose a **Deployment size** of **Small**, **Accelerator**
     as **None** (no GPU is needed for this setup), and allocate a **Cluster storage**
-    space of **20GB** (By Default Selected).
+    space of **20GB** (Selected By Default).
 
     !!! tip "Tip"
 

docs/openshift-ai/other-projects/RAG-talk-with-your-pdf.md

@@ -74,7 +74,7 @@ be populated with documents.
     **TensorFlow** image with Recommended Version (selected by default), choose
     a **Deployment size** of **Medium**, **Accelerator** as **None** (no GPU is
     needed for this setup) and allocate a **Cluster storage** space of **20GB**
-    (By Default Selected).
+    (Selected By Default).
 
     !!! tip "Tip"
 

docs/openshift-ai/other-projects/configure-jupyter-notebook-use-gpus-aiml-modeling.md

@@ -23,7 +23,7 @@ and Number of accelerators (GPUs).
 For our example project, let's name it "PyTorch Workbench". We'll select the
 **PyTorch** image, choose a **Deployment size** of **Small**, choose **Accelerator**
 of **NVIDIA V100 GPU**, **Number of accelerators** as **1**, and allocate a
-**Cluster storage** space of **20GB** (By Default Selected).
+**Cluster storage** space of **20GB** (Selected By Default).
 
 !!! tip "Hardware Acceleration using GPU"
 

docs/openshift-ai/other-projects/fraud-detection-predictive-ai-app.md

@@ -622,7 +622,7 @@ For our example project, let's name it "Fraud detection". We'll select the
 **TensorFlow** image with Recommended Version (selected by default), choose
 a **Deployment size** of **Small**, choose **Accelerator** of
 **NVIDIA V100 GPU**, **Number of accelerators** as **1**, and allocate
-a **Cluster storage** space of **20GB** (By Default Selected).
+a **Cluster storage** space of **20GB** (Selected By Default).
 
 Here, you will use **Environment Variables** to specify the Key/Value pairs related
 to the S3-compatible object storage bucket for storing your model.

docs/openshift-ai/other-projects/object-detection-app-using-yolo5.md

@@ -552,7 +552,7 @@ For our example project, let's name it "Fraud detection". We'll select the
 **PyTorch** image with Recommended Version (selected by default), choose
 a **Deployment size** of **Medium**, choose **Accelerator** of
 **NVIDIA V100 GPU**, **Number of accelerators** as **1**, and allocate
-a **Cluster storage** space of **20GB** (By Default Selected).
+a **Cluster storage** space of **20GB** (Selected By Default).
 
 Here, you will use **Environment Variables** to specify the Key/Value pairs related
 to the S3-compatible object storage bucket for storing your model.

docs/other-tools/R/rstudio-server/running-rstudio-on-NERC.md

@@ -17,7 +17,7 @@
     For our example project, let's name it "RStudio Workbench". We'll select the
     **RStudio** image, choose a **Deployment size** of **Small**, choose **Accelerator**
     of **None** (no GPU is needed for this setup), and allocate a **Cluster storage**
-    space of **20GB** (By Default Selected).
+    space of **20GB** (Selected By Default).
 
     !!! tip "Tip"